In their seminal work, [1]} introduce the Diffusion Probabilistic Model. This model is applied to various domains, such as time series and images. The main drawback in the proposed model is that it needs up to thousands of iterative steps to generate a valid data sample. [2]} proposed a diffusion generative model based on Langevin dynamics and the score matching method [3]}. The model estimates the Stein score function [4]} which is the logarithm of data density. Given the Stein score function, the model can generate data points.
